Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Gorelik, Natalia; Chong, Jaron; Lin, Dana J

doi:10.1055/s-0039-3400266

Cited by 14 publications

(8 citation statements)

References 69 publications

(102 reference statements)

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“…All 38 articles addressed an application of ML or DL with imaging data of MSK malignancies. Three review articles were found and excluded from statistical analysis [ 8 , 14 , 25 ]. 75.7% (28) of the studies were conducted retrospectively, 8.1% (3) were conducted prospectively and 16.2% (6) did not clearly state the study design.…”

Section: Resultsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. Key Points • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

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Section: Resultsmentioning

confidence: 99%

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

et al. 2022

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“…Clinical imaging of degenerative pathological features in disease processes yields invaluable information relevant to the diagnosis, management or development of therapeutic measures to treat such diseases. 261 AI and DML are particularly well suited to the evaluation of large data sets in clinical imaging. A deep convolutional neural network trained on a large, manually evaluated data set of 1599 patients and 7948 IVDs has been developed and outperforms human evaluations of MRI data.…”

Section: How Ai and Dml Have Im...mentioning

confidence: 99%

Section: How Ai and Dml Have Improved Clinical Spinal Imagingmentioning

confidence: 99%

An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics

et al. 2023

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Animal models have been invaluable in the identification of molecular events occurring in and contributing to intervertebral disc (IVD) degeneration and important therapeutic targets have been identified. Some outstanding animal models (murine, ovine, chondrodystrophoid canine) have been identified with their own strengths and weaknesses. The llama/alpaca, horse and kangaroo have emerged as new large species for IVD studies, and only time will tell if they will surpass the utility of existing models. The complexity of IVD degeneration poses difficulties in the selection of the most appropriate molecular target of many potential candidates, to focus on in the formulation of strategies to effect disc repair and regeneration. It may well be that many therapeutic objectives should be targeted simultaneously to effect a favorable outcome in human IVD degeneration. Use of animal models in isolation will not allow resolution of this complex issue and a paradigm shift and adoption of new methodologies is required to provide the next step forward in the determination of an effective repairative strategy for the IVD. AI has improved the accuracy and assessment of spinal imaging supporting clinical diagnostics and research efforts to better understand IVD degeneration and its treatment. Implementation of AI in the evaluation of histology data has improved the usefulness of a popular murine IVD model and could also be used in an ovine histopathological grading scheme that has been used to quantify degenerative IVD changes and stem cell mediated regeneration. These models are also attractive candidates for the evaluation of novel anti‐oxidant compounds that counter inflammatory conditions in degenerate IVDs and promote IVD regeneration. Some of these compounds also have pain‐relieving properties. AI has facilitated development of facial recognition pain assessment in animal IVD models offering the possibility of correlating the potential pain alleviating properties of some of these compounds with IVD regeneration.

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“…13 However, introducing deep learning-based techniques to the extensive quality ground-truth training datasets is essential for the development of accurate algorithms. 47,48 Also, ethical dilemmas should be taken into consideration. When dealing with systems that operate with enormous amounts of data, patients' privacy as well as human dignity may be jeopardized, unless meticulous safety mechanisms are implemented.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Artificial intelligence in musculoskeletal oncological radiology

Vogrin

Trojner

Kelc

2020

Radiology and Oncology

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BackgroundDue to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms.ConclusionsAlthough there is still a shortage of long-term studies confirming its benefits, there is probably a considerable potential for further development of computer-based expert systems aiming at a more efficient diagnosis of bone and soft tissue tumors.

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Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

Cited by 14 publications

References 69 publications

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies—a scoping review

An update on animal models of intervertebral disc degeneration and low back pain: Exploring the potential of artificial intelligence to improve research analysis and development of prospective therapeutics

Artificial intelligence in musculoskeletal oncological radiology

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